Overview

Dataset statistics

Number of variables5
Number of observations192
Missing cells19
Missing cells (%)2.0%
Duplicate rows4
Duplicate rows (%)2.1%
Total size in memory7.6 KiB
Average record size in memory40.7 B

Variable types

Categorical2
Text2
DateTime1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21723/S/1/datasetView.do

Alerts

Dataset has 4 (2.1%) duplicate rowsDuplicates
소재지 has 19 (9.9%) missing valuesMissing

Reproduction

Analysis started2024-04-29 21:10:46.994433
Analysis finished2024-04-29 21:10:48.456930
Duration1.46 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct23
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
성동구
33 
강서구
28 
강남구
24 
종로구
11 
영등포구
10 
Other values (18)
86 

Length

Max length4
Median length3
Mean length3.0833333
Min length2

Unique

Unique3 ?
Unique (%)1.6%

Sample

1st row종로구
2nd row종로구
3rd row종로구
4th row종로구
5th row종로구

Common Values

ValueCountFrequency (%)
성동구 33
17.2%
강서구 28
14.6%
강남구 24
12.5%
종로구 11
 
5.7%
영등포구 10
 
5.2%
은평구 9
 
4.7%
서초구 9
 
4.7%
금천구 9
 
4.7%
송파구 8
 
4.2%
중구 7
 
3.6%
Other values (13) 44
22.9%

Length

2024-04-30T06:10:48.525235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성동구 33
17.2%
강서구 28
14.6%
강남구 24
12.5%
종로구 11
 
5.7%
영등포구 10
 
5.2%
은평구 9
 
4.7%
서초구 9
 
4.7%
금천구 9
 
4.7%
송파구 8
 
4.2%
중구 7
 
3.6%
Other values (13) 44
22.9%

사업장명
Text

Distinct169
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-04-30T06:10:48.708146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length19
Mean length8.1458333
Min length2

Characters and Unicode

Total characters1564
Distinct characters292
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)79.2%

Sample

1st row케이비와이즈스타일반사모부동산투자신탁제17호(적격)
2nd row㈜에스엘플랫폼 서머셋팰리스서울 지점
3rd row주식회사 하나은행(종로타워)
4th row적선현대빌딩 관리단
5th row더원
ValueCountFrequency (%)
주식회사 7
 
3.0%
주)특수건설 4
 
1.7%
주)성보씨엔이(불광동 4
 
1.7%
동양도장 3
 
1.3%
영진정밀화학(주 3
 
1.3%
노원점 2
 
0.9%
현대오일뱅크(주)직영 2
 
0.9%
오스템임플란트주식회사 2
 
0.9%
대우판금 2
 
0.9%
주)금천서부자동차 2
 
0.9%
Other values (191) 202
86.7%
2024-04-30T06:10:49.037757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 87
 
5.6%
( 74
 
4.7%
) 74
 
4.7%
41
 
2.6%
스 36
 
2.3%
동 36
 
2.3%
차 35
 
2.2%
사 29
 
1.9%
자 25
 
1.6%
대 25
 
1.6%
Other values (282) 1102
70.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1332
85.2%
Open Punctuation 74
 
4.7%
Close Punctuation 74
 
4.7%
Space Separator 41
 
2.6%
Lowercase Letter 18
 
1.2%
Decimal Number 14
 
0.9%
Uppercase Letter 6
 
0.4%
Other Symbol 3
 
0.2%
Dash Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 87
 
6.5%
스 36
 
2.7%
동 36
 
2.7%
차 35
 
2.6%
사 29
 
2.2%
자 25
 
1.9%
대 25
 
1.9%
이 24
 
1.8%
성 21
 
1.6%
서 20
 
1.5%
Other values (252) 994
74.6%
Lowercase Letter
ValueCountFrequency (%)
a 4
22.2%
r 3
16.7%
h 3
16.7%
e 2
11.1%
i 1
 
5.6%
t 1
 
5.6%
b 1
 
5.6%
w 1
 
5.6%
s 1
 
5.6%
o 1
 
5.6%
Decimal Number
ValueCountFrequency (%)
2 5
35.7%
1 3
21.4%
7 1
 
7.1%
9 1
 
7.1%
5 1
 
7.1%
3 1
 
7.1%
6 1
 
7.1%
4 1
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
Y 1
16.7%
J 1
16.7%
T 1
16.7%
F 1
16.7%
C 1
16.7%
G 1
16.7%
Open Punctuation
ValueCountFrequency (%)
( 74
100.0%
Close Punctuation
ValueCountFrequency (%)
) 74
100.0%
Space Separator
ValueCountFrequency (%)
41
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
# 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1335
85.4%
Common 205
 
13.1%
Latin 24
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 87
 
6.5%
스 36
 
2.7%
동 36
 
2.7%
차 35
 
2.6%
사 29
 
2.2%
자 25
 
1.9%
대 25
 
1.9%
이 24
 
1.8%
성 21
 
1.6%
서 20
 
1.5%
Other values (253) 997
74.7%
Latin
ValueCountFrequency (%)
a 4
16.7%
r 3
12.5%
h 3
12.5%
e 2
 
8.3%
Y 1
 
4.2%
J 1
 
4.2%
T 1
 
4.2%
F 1
 
4.2%
i 1
 
4.2%
t 1
 
4.2%
Other values (6) 6
25.0%
Common
ValueCountFrequency (%)
( 74
36.1%
) 74
36.1%
41
20.0%
2 5
 
2.4%
1 3
 
1.5%
7 1
 
0.5%
- 1
 
0.5%
9 1
 
0.5%
# 1
 
0.5%
5 1
 
0.5%
Other values (3) 3
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1332
85.2%
ASCII 229
 
14.6%
None 3
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 87
 
6.5%
스 36
 
2.7%
동 36
 
2.7%
차 35
 
2.6%
사 29
 
2.2%
자 25
 
1.9%
대 25
 
1.9%
이 24
 
1.8%
성 21
 
1.6%
서 20
 
1.5%
Other values (252) 994
74.6%
ASCII
ValueCountFrequency (%)
( 74
32.3%
) 74
32.3%
41
17.9%
2 5
 
2.2%
a 4
 
1.7%
r 3
 
1.3%
h 3
 
1.3%
1 3
 
1.3%
e 2
 
0.9%
7 1
 
0.4%
Other values (19) 19
 
8.3%
None
ValueCountFrequency (%)
㈜ 3
100.0%

소재지
Text

MISSING 

Distinct147
Distinct (%)85.0%
Missing19
Missing (%)9.9%
Memory size1.6 KiB
2024-04-30T06:10:49.324205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length35
Mean length21.439306
Min length13

Characters and Unicode

Total characters3709
Distinct characters201
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)74.6%

Sample

1st row서울특별시 종로구 내자동 219
2nd row서울특별시 종로구 수송동
3rd row서울특별시 종로구 종로2가 6
4th row서울특별시 종로구 적선동 80 적선현대빌딩
5th row서울특별시 종로구 연지동 59 3층
ValueCountFrequency (%)
서울특별시 173
 
22.7%
강서구 28
 
3.7%
성동구 26
 
3.4%
강남구 21
 
2.8%
성수동2가 15
 
2.0%
용답동 11
 
1.4%
종로구 11
 
1.4%
은평구 9
 
1.2%
금천구 9
 
1.2%
독산동 9
 
1.2%
Other values (286) 449
59.0%
2024-04-30T06:10:49.749270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
588
 
15.9%
서 219
 
5.9%
동 209
 
5.6%
구 181
 
4.9%
울 178
 
4.8%
특 173
 
4.7%
별 173
 
4.7%
시 173
 
4.7%
2 157
 
4.2%
- 142
 
3.8%
Other values (191) 1516
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2191
59.1%
Decimal Number 763
 
20.6%
Space Separator 588
 
15.9%
Dash Punctuation 142
 
3.8%
Uppercase Letter 12
 
0.3%
Other Punctuation 5
 
0.1%
Close Punctuation 4
 
0.1%
Open Punctuation 3
 
0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 219
 
10.0%
동 209
 
9.5%
구 181
 
8.3%
울 178
 
8.1%
특 173
 
7.9%
별 173
 
7.9%
시 173
 
7.9%
강 54
 
2.5%
성 52
 
2.4%
가 41
 
1.9%
Other values (165) 738
33.7%
Decimal Number
ValueCountFrequency (%)
2 157
20.6%
1 112
14.7%
3 90
11.8%
4 75
9.8%
6 73
9.6%
0 59
 
7.7%
7 56
 
7.3%
8 53
 
6.9%
5 44
 
5.8%
9 44
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
E 2
16.7%
W 2
16.7%
I 1
8.3%
F 1
8.3%
N 1
8.3%
R 1
8.3%
T 1
8.3%
O 1
8.3%
M 1
8.3%
B 1
8.3%
Space Separator
ValueCountFrequency (%)
588
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 142
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2191
59.1%
Common 1505
40.6%
Latin 13
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 219
 
10.0%
동 209
 
9.5%
구 181
 
8.3%
울 178
 
8.1%
특 173
 
7.9%
별 173
 
7.9%
시 173
 
7.9%
강 54
 
2.5%
성 52
 
2.4%
가 41
 
1.9%
Other values (165) 738
33.7%
Common
ValueCountFrequency (%)
588
39.1%
2 157
 
10.4%
- 142
 
9.4%
1 112
 
7.4%
3 90
 
6.0%
4 75
 
5.0%
6 73
 
4.9%
0 59
 
3.9%
7 56
 
3.7%
8 53
 
3.5%
Other values (5) 100
 
6.6%
Latin
ValueCountFrequency (%)
E 2
15.4%
W 2
15.4%
I 1
7.7%
F 1
7.7%
N 1
7.7%
R 1
7.7%
T 1
7.7%
O 1
7.7%
Ⅱ 1
7.7%
M 1
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2191
59.1%
ASCII 1517
40.9%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
588
38.8%
2 157
 
10.3%
- 142
 
9.4%
1 112
 
7.4%
3 90
 
5.9%
4 75
 
4.9%
6 73
 
4.8%
0 59
 
3.9%
7 56
 
3.7%
8 53
 
3.5%
Other values (15) 112
 
7.4%
Hangul
ValueCountFrequency (%)
서 219
 
10.0%
동 209
 
9.5%
구 181
 
8.3%
울 178
 
8.1%
특 173
 
7.9%
별 173
 
7.9%
시 173
 
7.9%
강 54
 
2.5%
성 52
 
2.4%
가 41
 
1.9%
Other values (165) 738
33.7%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

업종
Categorical

Distinct44
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
<NA>
37 
자동차 세차업
32 
자동차 종합 수리업
21 
자동차 전문 수리업
10 
교량, 터널 및 철도 건설업
 
8
Other values (39)
84 

Length

Max length21
Median length20
Mean length8.21875
Min length2

Unique

Unique20 ?
Unique (%)10.4%

Sample

1st row부동산 임대업
2nd row호텔업
3rd row부동산업
4th row부동산 임대업
5th row도금업

Common Values

ValueCountFrequency (%)
<NA> 37
19.3%
자동차 세차업 32
16.7%
자동차 종합 수리업 21
 
10.9%
자동차 전문 수리업 10
 
5.2%
교량, 터널 및 철도 건설업 8
 
4.2%
자동차 수리 및 세차업 7
 
3.6%
부동산 임대업 6
 
3.1%
자동차 수리업 6
 
3.1%
부동산업 5
 
2.6%
주유소 운영업 5
 
2.6%
Other values (34) 55
28.6%

Length

2024-04-30T06:10:49.898848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차 80
17.4%
수리업 40
 
8.7%
세차업 39
 
8.5%
na 37
 
8.0%
및 34
 
7.4%
종합 23
 
5.0%
전문 10
 
2.2%
부동산 9
 
2.0%
운영업 9
 
2.0%
터널 8
 
1.7%
Other values (69) 171
37.2%

점검일자
Date

Distinct105
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum2021-05-25 00:00:00
Maximum2023-02-23 00:00:00
2024-04-30T06:10:50.005263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:10:50.122901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Correlations

2024-04-30T06:10:50.206353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분업종
구분1.0000.895
업종0.8951.000
2024-04-30T06:10:50.293750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종구분
업종1.0000.399
구분0.3991.000
2024-04-30T06:10:50.370044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분업종
구분1.0000.399
업종0.3991.000

Missing values

2024-04-30T06:10:48.310534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T06:10:48.422950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

구분사업장명소재지업종점검일자
0종로구케이비와이즈스타일반사모부동산투자신탁제17호(적격)서울특별시 종로구 내자동 219부동산 임대업2022.03.03
1종로구㈜에스엘플랫폼 서머셋팰리스서울 지점서울특별시 종로구 수송동호텔업2022.05.12
2종로구주식회사 하나은행(종로타워)서울특별시 종로구 종로2가 6부동산업2022.12.13
3종로구적선현대빌딩 관리단서울특별시 종로구 적선동 80 적선현대빌딩부동산 임대업2022.06.07
4종로구더원서울특별시 종로구 연지동 59 3층도금업2022.04.21
5종로구보람서울특별시 종로구 봉익동 40-1<NA>2021.05.25
6종로구세란병원서울특별시 종로구 무악동 32-2<NA>2022.11.03
7종로구(주)뉴실크로드서울특별시 종로구 연지동 206 연지빌딩<NA>2022.03.08
8종로구골드웨이서울특별시 종로구 관수동 21-4 관일빌딩귀금속 장신구 및 관련제품 제조업2022.03.08
9종로구보람서울특별시 종로구 봉익동 40-1<NA>2022.05.25
구분사업장명소재지업종점검일자
182송파구(주)엠씨모터스팩토리서울특별시 송파구 장지동 875 서울복합물류자동차 수리업2022.08.08
183송파구(주)일원현대자동차서울특별시 송파구 방이동 212-2자동차 수리업2022.04.20
184송파구현대오일뱅크(주)직영 서원주유소서울특별시 송파구 가락동 146-6차량용 주유소 운영업2022.04.12
185송파구저스트 디테일서울특별시 송파구 가락동 109-8자동차 세차업2022.12.15
186송파구저스트 디테일서울특별시 송파구 가락동 109-8자동차 세차업2022.10.07
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